# coding: utf-8

# ## A PyTorch CNN for training CIFAR-10 using Co-Teaching.
# Code adapted from: https://github.com/bhanML/Co-teaching/blob/master/model.py
# This code requires you have PyTorch installed
# See: https://pytorch.org/get-started/locally/


# Python 2 and 3 compatibility
from __future__ import (
    print_function, absolute_import, division, unicode_literals, with_statement)

import torch.nn as nn
import torch.nn.functional as F


def call_bn(bn, x):
    return bn(x)


class CNN(nn.Module):
    def __init__(self, input_channel=3, n_outputs=10, dropout_rate=0.25,
                 top_bn=False):
        self.dropout_rate = dropout_rate
        self.top_bn = top_bn
        super(CNN, self).__init__()
        self.c1 = nn.Conv2d(
            input_channel, 128, kernel_size=3, stride=1, padding=1)
        self.c2 = nn.Conv2d(128, 128, kernel_size=3, stride=1, padding=1)
        self.c3 = nn.Conv2d(128, 128, kernel_size=3, stride=1, padding=1)
        self.c4 = nn.Conv2d(128, 256, kernel_size=3, stride=1, padding=1)
        self.c5 = nn.Conv2d(256, 256, kernel_size=3, stride=1, padding=1)
        self.c6 = nn.Conv2d(256, 256, kernel_size=3, stride=1, padding=1)
        self.c7 = nn.Conv2d(256, 512, kernel_size=3, stride=1, padding=0)
        self.c8 = nn.Conv2d(512, 256, kernel_size=3, stride=1, padding=0)
        self.c9 = nn.Conv2d(256, 128, kernel_size=3, stride=1, padding=0)
        self.l_c1 = nn.Linear(128, n_outputs)
        self.bn1 = nn.BatchNorm2d(128)
        self.bn2 = nn.BatchNorm2d(128)
        self.bn3 = nn.BatchNorm2d(128)
        self.bn4 = nn.BatchNorm2d(256)
        self.bn5 = nn.BatchNorm2d(256)
        self.bn6 = nn.BatchNorm2d(256)
        self.bn7 = nn.BatchNorm2d(512)
        self.bn8 = nn.BatchNorm2d(256)
        self.bn9 = nn.BatchNorm2d(128)

    def forward(self, x, ):
        h = x
        h = self.c1(h)
        h = F.leaky_relu(call_bn(self.bn1, h), negative_slope=0.01)
        h = self.c2(h)
        h = F.leaky_relu(call_bn(self.bn2, h), negative_slope=0.01)
        h = self.c3(h)
        h = F.leaky_relu(call_bn(self.bn3, h), negative_slope=0.01)
        h = F.max_pool2d(h, kernel_size=2, stride=2)
        h = F.dropout2d(h, p=self.dropout_rate)

        h = self.c4(h)
        h = F.leaky_relu(call_bn(self.bn4, h), negative_slope=0.01)
        h = self.c5(h)
        h = F.leaky_relu(call_bn(self.bn5, h), negative_slope=0.01)
        h = self.c6(h)
        h = F.leaky_relu(call_bn(self.bn6, h), negative_slope=0.01)
        h = F.max_pool2d(h, kernel_size=2, stride=2)
        h = F.dropout2d(h, p=self.dropout_rate)

        h = self.c7(h)
        h = F.leaky_relu(call_bn(self.bn7, h), negative_slope=0.01)
        h = self.c8(h)
        h = F.leaky_relu(call_bn(self.bn8, h), negative_slope=0.01)
        h = self.c9(h)
        h = F.leaky_relu(call_bn(self.bn9, h), negative_slope=0.01)
        h = F.avg_pool2d(h, kernel_size=h.data.shape[2])

        h = h.view(h.size(0), h.size(1))
        logit = self.l_c1(h)
        if self.top_bn:
            logit = call_bn(self.bn_c1, logit)
        return logit
